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README.md

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 *Simulated data for Intent testing, does not use real Neuralink/BCI hardware signals.*
 
-BCI Intent Data Study and Testing (conceptual early design)
+BCI Intent Data Study and Testing (conceptual early design) for training machine learning models for neural signal 
+decoding without needing large scale real hardware BCI datasets, addressing data scarcity and privacy issues around BCI intent studies.
 
-Training machine learning models for neural signal decoding without needing large real hardware BCI datasets, addressing data scarcity and privacy issues around BCI intent studies.
+-Augmenting real-world BCI data with synthetic samples to improve model robustness and diversity, as in GAN-based approaches.
 
-Augmenting real-world BCI data with synthetic samples to improve model robustness and diversity, as in GAN-based approaches.
+-Testing and calibrating BCI systems for motor imagery tasks like prosthetic control before human trials.
 
-Testing and calibrating BCI systems for motor imagery tasks like prosthetic control before human trials.
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-Simulating neural responses in assistive technologies for disabled individuals, enabling faster iteration in labs like Neuralink.
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-Developing predictive models for intent recognition in human-AI interactions and rehabilitative BCIs.
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-Enhancing clinical research datasets for disease risk assessment and patient outcome prediction in neuroengineering.
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-Validating algorithms in frontier labs (e.g., Neuralink, Paradromics) for high-data-rate implants by generating idealized signals.
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-This dataset contains high-bandwidth neural training data collected from BCI-FPS, a specialized training platform for brain-computer interface research.
+-Simulating neural responses in assistive technologies for disabled individuals, enabling faster iteration in labs like Neuralink.
 
 ## Dataset Summary